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宏基因组学研究的网络资源

Web Resources for Metagenomics Studies.

作者信息

Dudhagara Pravin, Bhavsar Sunil, Bhagat Chintan, Ghelani Anjana, Bhatt Shreyas, Patel Rajesh

机构信息

Department of Biosciences, Veer Narmad South Gujarat University, Surat 395007, India.

Department of Biosciences, Veer Narmad South Gujarat University, Surat 395007, India.

出版信息

Genomics Proteomics Bioinformatics. 2015 Oct;13(5):296-303. doi: 10.1016/j.gpb.2015.10.003. Epub 2015 Nov 18.


DOI:10.1016/j.gpb.2015.10.003
PMID:26602607
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4678780/
Abstract

The development of next-generation sequencing (NGS) platforms spawned an enormous volume of data. This explosion in data has unearthed new scalability challenges for existing bioinformatics tools. The analysis of metagenomic sequences using bioinformatics pipelines is complicated by the substantial complexity of these data. In this article, we review several commonly-used online tools for metagenomics data analysis with respect to their quality and detail of analysis using simulated metagenomics data. There are at least a dozen such software tools presently available in the public domain. Among them, MGRAST, IMG/M, and METAVIR are the most well-known tools according to the number of citations by peer-reviewed scientific media up to mid-2015. Here, we describe 12 online tools with respect to their web link, annotation pipelines, clustering methods, online user support, and availability of data storage. We have also done the rating for each tool to screen more potential and preferential tools and evaluated five best tools using synthetic metagenome. The article comprehensively deals with the contemporary problems and the prospects of metagenomics from a bioinformatics viewpoint.

摘要

下一代测序(NGS)平台的发展产生了海量数据。数据的这种爆炸式增长给现有的生物信息学工具带来了新的可扩展性挑战。使用生物信息学流程分析宏基因组序列因这些数据的高度复杂性而变得复杂。在本文中,我们使用模拟宏基因组数据,就其质量和分析细节,综述了几种常用的宏基因组学数据分析在线工具。目前在公共领域至少有十几种这样的软件工具。其中,根据截至2015年年中同行评审科学媒体的引用次数,MGRAST、IMG/M和METAVIR是最知名的工具。在此,我们描述了12种在线工具的网络链接、注释流程、聚类方法、在线用户支持和数据存储可用性。我们还对每个工具进行了评级,以筛选出更具潜力和更优的工具,并使用合成宏基因组评估了五个最佳工具。本文从生物信息学的角度全面探讨了宏基因组学的当代问题和前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd7f/4678780/c1bc96a98651/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd7f/4678780/c1bc96a98651/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd7f/4678780/c1bc96a98651/gr1.jpg

相似文献

[1]
Web Resources for Metagenomics Studies.

Genomics Proteomics Bioinformatics. 2015-10

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[3]
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[7]
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[8]
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[9]
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[10]
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The Value and Significance of Metagenomics of Marine Environments. Preface.

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本文引用的文献

[1]
Shotgun metagenomic sequencing based microbial diversity assessment of Lasundra hot spring, India.

Genom Data. 2015-3-24

[2]
Cultivation-independent comprehensive survey of bacterial diversity in Tulsi Shyam Hot Springs, India.

Genom Data. 2015-3-18

[3]
Bacterial tag encoded FLX titanium amplicon pyrosequencing (bTEFAP) based assessment of prokaryotic diversity in metagenome of Lonar soda lake, India.

Genom Data. 2015-2-7

[4]
Metagenomic sequence of saline desert microbiota from wild ass sanctuary, Little Rann of Kutch, Gujarat, India.

Genom Data. 2015-1-15

[5]
Metagenomic data of fungal internal transcribed Spacer and 18S rRNA gene sequences from Lonar lake sediment, India.

Data Brief. 2015-6-18

[6]
Metagenomic surveys of gut microbiota.

Genomics Proteomics Bioinformatics. 2015-6

[7]
Pearls and pitfalls of genomics-based microbiome analysis.

Emerg Microbes Infect. 2012-12

[8]
Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies.

Bioinform Biol Insights. 2015-5-5

[9]
CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers.

BMC Genomics. 2015-3-25

[10]
Web resources for microbial data.

Genomics Proteomics Bioinformatics. 2015-2

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